[1]} developed a Bayesian approach to jointly estimate a rheological field and a basal sliding parameter field. The approach is similar to that of [2]}, except that the rheological parameter is also allowed to vary spatially. The scale of the problem addressed is also larger since the methodology is applied to a large marine-terminating glacier in Greenland. To alleviate computational issues, a variational inference scheme [3]} was used instead of MCMC for evaluation of an approximate posterior distribution. A rank-reduction technique, inspired by [4]}, was also used to facilitate computations with large covariance matrices. In contrast to many previous works, [1]} used a log-normal distribution for the data model. Moreover, a Gaussian process prior with a squared-exponential kernel was used for both the rheological and basal sliding fields. This paper breaks ground with the use of variational inference instead of MCMC; variational inference has been seeing increased used in recent years in geophysical applications where computational efficiency needs to be addressed.
